This tool is run for one experiment at a time. The user needs to provide the project name and the experiment name to the tool.

This tool, by default, works for all of the nodes corresponding to the given experiment. However, it can be made to work with a restricted set of nodes, either by directly providing the set of interested nodes, or by providing an AAL (experiment procedure) file to fetch the set of desired nodes.

This tool by default only informs if the MAGI daemon process on a node is reachable or not. Specific options can be used to fetch group membership details and information about active agents.

If you want to reboot the MAGI daemon, magi_status.py first reboots MAGI daemon processes on the experiment nodes, and then fetches their status.

If the tool is asked to download logs, it just does that, and does not fetch the status.

Usage: magi_status.py [options]
Script to get the status of MAGI daemon processes on experiment nodes,
to reboot them if required, and to download logs.
Options:
-h, --help show this help message and exit
-p PROJECT, --project=PROJECT
Project name
-e EXPERIMENT, --experiment=EXPERIMENT
Experiment name
-n NODES, --nodes=NODES
Comma-separated list of the nodes to reboot MAGI
daemon
-a AAL, --aal=AAL The yaml-based procedure file to extract the list of
nodes
-l, --logs Fetch logs. The -o/--logoutdir option is applicable
only when fetching logs.
-o LOGOUTDIR, --logoutdir=LOGOUTDIR
Store logs under the given directory. Default: /tmp
-g, --groupmembership
Fetch group membership detail
-i, --agentinfo Fetch loaded agent information
-t TIMEOUT, --timeout=TIMEOUT
Number of seconds to wait to receive the status reply
from the nodes on the overlay
-r, --reboot Reboot nodes. The following options are applicable
only when rebooting.
-d DISTPATH, --distpath=DISTPATH
Location of the distribution
-U, --noupdate Do not update the system before installing MAGI
-N, --noinstall Do not install MAGI and the supporting libraries

magi_graph.py is a graph generator for experiments executed on CREATE using MAGI. The tool fetches the required data using MAGI’s data management layer and generates a graph in PNG format. This tool may be executed from either the Deter Ops machine or a remote computer with access to internet. The data to be plotted and other graph features are configurable.

The various commandline options are as follows:

Usage: magi_graph.py [options]
Plots the graph for an experiment based on parameters provided.
Experiment Configuration File OR Project and Experiment Name
needs to be provided to be able to connect to the experiment.
Need to provide build a graph specific configuration for plotting.
Options:
-h, --help show this help message and exit
-e EXPERIMENT, --experiment=EXPERIMENT
Experiment name
-p PROJECT, --project=PROJECT
Project name
-x EXPERIMENTCONFIG, --experimentConfig=EXPERIMENTCONFIG
Experiment configuration file
-c CONFIG, --config=CONFIG
Graph configuration file
-a AGENT, --agent=AGENT
Agent name. This is used to fetch available database
fields
-l AAL, --aal=AAL AAL (experiment procedure) file. This is also used to
fetch available database fields
-o OUTPUT, --output=OUTPUT
Output graph file. Default: graph.png
-t, --tunnel Tell the tool to tunnel request through Deter Ops
(users.create.iucc.ac.il).
-u USERNAME, --username=USERNAME
Username for creating tunnel. Required only if
different from current shell username.

This tool expects the user to provide a configuration file. The format of the configuration file needs to be similar to the sample configuration file provided below.

The configuration is divided into two parts a) Graph options and b) Database options. Graph options are used to configure the type of graph and the various labels. The database options help the tool fetch the data to be plotted.

Each record stored in the database using MAGI’s database layer has the following three fields along with any other that an agent populates.

agent: Agent Name
host: Node of which the agent is hosted
created: Timestamp of when the record is created

In the above mentioned example, data populated by the agent named “monitor_agent” hosted on the node named “servernode” will be fetched. The data would further be filtered on the configured values of peerNode and trafficDirection, which are agent specific fields.

Among the fetched data, values corresponding to the fields, created and bytes, will be plotted correspoding to the x and the y axis, respectively.